Bio Entrez Link

Version Compatibility

Reference examples tested with: BioPython 1.83+, Entrez Direct 21.0+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show biopython then help(Bio.Entrez.elink) to check signatures
• CLI: elink -version then elink -help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Entrez Link

"Find records linked to this record in another NCBI database" -> ELink walks the curated, weekly-maintained link tables between Entrez databases. A link is an asserted relationship (e.g. "this PubMed article describes this nucleotide sequence"), not a similarity hit.

ELink is the navigation layer of Entrez. The decision that matters most is which linkname to use — not which databases. A single (dbfrom, db) pair can have a dozen linkname variants distinguishing curation level, evidence type, and direction. Picking the wrong one is the difference between 5 high-confidence matches and 500 noisy automated assertions.

• Python: Entrez.elink(dbfrom=..., db=..., id=..., linkname=...) (BioPython)
• CLI: elink -db pubmed -target gene -name pubmed_gene_rif (Entrez Direct)
• R: entrez_link(dbfrom=..., db=..., id=...) (rentrez)

Required Setup

from Bio import Entrez
Entrez.email = 'researcher@institution.edu'
Entrez.api_key = 'optional_api_key'  # raises rate to 10 req/sec

The linkname decision (most important)

For most (dbfrom, db) pairs NCBI exposes multiple link tables. The qualifiers in the name encode the curation level and the evidence source. Choose deliberately.

gene -> protein (representative example)

pubmed -> gene

nucleotide -> protein

Discover what link names exist for a pair

h = Entrez.elink(dbfrom='gene', db='protein', id='672', cmd='acheck')
record = Entrez.read(h); h.close()
for ls in record[0]['IdCheckList']['IdLinkSet'][0]['LinkInfo']:
    print(f'{ls["Name"]}  -> {ls["DbTo"]} | {ls["MenuTag"]} ({ls["HtmlTag"]})')

cmd='acheck' is the only authoritative way to enumerate available linknames — they change with each NCBI release.

Decision table: which cmd for which goal

The neighbor_history cmd is essential when source id count exceeds ~200 — past that, the URL-length limit makes the comma-joined form fail. With neighbor_history ELink puts results on the history server and returns WebEnv/QueryKey for downstream pickup.

Asymmetric link warning

ELink relationships are not guaranteed symmetric. pubmed_gene and gene_pubmed may return different sets because:

• Direction-dependent curation: gene-to-PubMed is curated by NCBI staff (GeneRIF); PubMed-to-gene includes text-mining.
• Cutoffs: some link tables truncate at N best links in one direction but not the other.
• Index lag asymmetry: when one db updates faster than the other.

If round-trip consistency matters (e.g. "every gene mentioned in this paper, then every paper mentioning each gene"), expect the round-trip set to be larger than the input — and never assume A -> B -> A returns the original ID alone.

Per-database link catalog (curated subset)

gene

nuccore / nucleotide

protein

pubmed

bioproject

Code patterns

Single source -> single target

Goal: Get RefSeq proteins for a single gene.

Approach: ELink with explicit linkname to restrict to curated set.

Reference (BioPython 1.83+):

def gene_to_refseq_proteins(gene_id):
    h = Entrez.elink(dbfrom='gene', db='protein', id=gene_id, linkname='gene_protein_refseq')
    r = Entrez.read(h); h.close()
    if not r[0]['LinkSetDb']:
        return []
    return [link['Id'] for link in r[0]['LinkSetDb'][0]['Link']]

print(gene_to_refseq_proteins('672'))  # BRCA1

Batch source -> target (small batch)

Goal: Get linked proteins for a list of <200 gene IDs in one call.

Approach: Comma-join IDs; one linkset per input in the response.

Reference (BioPython 1.83+):

def batch_gene_protein(gene_ids):
    h = Entrez.elink(dbfrom='gene', db='protein', id=','.join(gene_ids), linkname='gene_protein_refseq')
    r = Entrez.read(h); h.close()
    out = {}
    for linkset in r:
        src = linkset['IdList'][0]
        out[src] = [link['Id'] for link in linkset['LinkSetDb'][0]['Link']] if linkset['LinkSetDb'] else []
    return out

Large batch via history server

Goal: Link 5,000 gene IDs to proteins without hitting URL-length limits.

Approach: EPost the IDs first (chunked at 200), then ELink with cmd='neighbor_history' referencing the WebEnv. Downstream EFetch picks up linked IDs from the history server.

Reference (BioPython 1.83+):

def post_then_link(gene_ids, target='protein', linkname='gene_protein_refseq'):
    # EPost in chunks of 200
    webenv = None
    for i in range(0, len(gene_ids), 200):
        chunk = gene_ids[i:i+200]
        kwargs = {'db': 'gene', 'id': ','.join(chunk)}
        if webenv:
            kwargs['WebEnv'] = webenv
        h = Entrez.epost(**kwargs)
        r = Entrez.read(h); h.close()
        webenv = r['WebEnv']
        query_key = r['QueryKey']
        time.sleep(0.1 if Entrez.api_key else 0.34)

    # Link with neighbor_history
    h = Entrez.elink(dbfrom='gene', db=target, linkname=linkname,
                     cmd='neighbor_history', WebEnv=webenv, query_key=query_key)
    r = Entrez.read(h); h.close()
    # WebEnv is at the top level of the response; QueryKey is per-LinkSetDbHistory entry.
    return r[0]['WebEnv'], r[0]['LinkSetDbHistory'][0]['QueryKey']

we, qk = post_then_link(['672', '675', '7157'] * 1000)
# Downstream: Entrez.efetch(db='protein', WebEnv=we, query_key=qk, retstart=..., retmax=500)